Dynamics-based extraction of information sparsely encoded in high dimensional data streams

A major roadblock in taking full advantage of the recent exponential growth in data collection and actuation capabilities stems from the curse of dimensionality. Simply put, existing techniques are ill-equipped to deal with the resulting volume of data. The goal of this paper is to show how the use of simple dynamical systems concepts can lead to tractable, computationally efficient algorithms for extracting information sparsely encoded in extremely large data sets. In addition, as shown here, this approach leads to non-entropic information measures, better suited than the classical, entropy-based information theoretic measure, to problems where the information is by nature dynamic.